Found this a bit like chutes & ladders for genomics
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?document_name=HowToSubmit.pdf The #dbGaP submission process
Posts Tagged ‘#genomics’
The dbgap submission process
December 27, 2019Rumors of the death of consumer genomics are greatly exaggerated
December 22, 2018Blockchains: How They Work and Why They’ll Change the World – IEEE Spectrum
December 10, 2017#Blockchains: How They Work & Why They’ll Change the World
https://spectrum.IEEE.org/computing/networks/blockchains-how-they-work-and-why-theyll-change-the-world + https://spectrum.IEEE.org/computing/networks/do-you-need-a-blockchain Overview of the technology, with a focus piece on whether it applies to a particular domain. Not sure we need this in genomics…
Do You Need a Blockchain? – IEEE Spectrum
https://spectrum.ieee.org/computing/networks/do-you-need-a-blockchain
Co-directors of newly launched Harvard Data Science Initiative discuss new era
June 19, 2017fellowships, grants, space
QT:{{”
“DOMINICI: Because of the new advances in technology, almost every field right now has data, and more data than ever. Clearly, there’s the explosion of genetics and genomics data in the life sciences, in molecular data, as well as astronomy and economics. Even in the humanities, you can scan documents and turn it into data that you can analyze.
…
PARKES: To add some numbers to this, IBM has estimated that we’re generating more than one quintillion bytes of data a day. (A quintillion is a 10 to the 18th.)
…
DOMINICI: One of the reasons we are so excited that Harvard is launching the Data Science Initiative is because of all the advances our faculty have made in recent years. We can now describe the entire genome, define the exposome (the environmental analogue to the genome), characterize social interactions and mood via cellphone data, and can digitize historical data relevant for the humanities. ….
DOMINICI: We have launched the Harvard Data Science Postdoctoral Fellowship, which is among the largest programs of its kind, and we want to recruit talented individuals in a highly interdisciplinary ways.
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We have also launched a competitive research fund that will catalyze small research projects around the University. Through our friends in the Faculty of Arts and Sciences and the Medical School, we’ve identified some spaces in the near term where people can get together. …
PARKES: We are launching the initiative because we want to get to a point where we have a Harvard Data Science Institute. The aspiration is that the Data Science Institute will have some physical space associated with it,
…
Then the third one I wanted to mention is privacy.
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http://news.harvard.edu/gazette/story/2017/03/co-directors-of-newly-launched-harvard-data-science-initiative-discuss-new-era/
Inferring chromatin-bound protein complexes from genome-wide binding assays – Genome Research
February 26, 2017Inferring [w. NMF] chromatin-bound protein complexes [of TFs] from [ENCODE ChIP-seq] binding assays, by @ElementoLab
http://genome.cshlp.org/content/23/8/1295.full
Giannopoulou E, Elemento O. 2013. Inferring chromatin-bound
protein complexes from genome-wide binding assays. Genome Research, Published in Advance April 3, 2013, doi: 10.1101/gr.149419.112.
This study uses nonnegative matrix factorization (NMF) of ENCODE CHIP-seq data (transcription
factors and histone modifications) to predict complexes of
transcription factors that bind DNA
together; it then assesses how these predicted complexes regulate gene expression. It goes beyond
previous studies in that it attempts to treat the TFs as complexes rather than individuals. A handful of
the predicted complexes correspond to known regulatory complexes, e.g. PRC2, and overall, the
complexes were enriched for known protein-protein interactions. Linear regression and random forest
models were then used to predict the effects of the complexes on the expression of adjacent genes. In
both models, the complexes performed better than those predicted from a scrambled TF read count
matrix. Overall, this study provides a large set of hypotheses for combinations of TFs that may
function together, as well as potential new components of known complexes.
The Big Fight Over Fossils
August 7, 2016Big Fight Over Fossils
http://www.NewYorker.com/magazine/2016/06/27/lee-berger-digs-for-bones-and-glory #Paleoanthropology issues: open v closed data, scholarship v showmanship. Genomics parallels
Illumina announce new CEO | Front Line Genomics
March 18, 2016.@Illumina announces new CEO
http://www.frontlinegenomics.com/3534/illumina-announce-new-ceo Illuminating news. Congrats to @fdesouza at his new position, important for #genomics
PLOS Genetics: A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures
February 27, 2016Model-Based Approach to Inferring…#Cancer Mutation Signatures http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005657 Assuming independence betw 3 NTs, 11 v 95 parameters
QT:{{”
The first contribution of this paper is to suggest a more parsimonious approach to modelling mutation signatures, with the benefit of producing both more stable estimates and more easily interpretable signatures. In brief, we substantially reduce the number of parameters per signature by breaking each mutation pattern into “features”, and assuming independence across mutation features. For example, consider the case where a mutation pattern is defined by the substitution and its two flanking bases. We break this into three features
(substitution, 3′ base, 5′ base), and characterize each mutation signature by a probability distribution for each feature (which, by our independence assumption, are multiplied together to define a distribution on mutation patterns). Since the number of possible values for each feature is 6, 4, and 4 respectively this requires 5 + 3 + 3 = 11 parameters instead of 96 − 1 = 95 parameters. Furthermore, extending this model to account for ±n neighboring bases requires only 5 + 6nparameters instead of 6 × 42n − 1. For example, considering ±2 positions requires 17 parameters instead of 1,535. Finally,
incorporating transcription strand as an additional feature adds just one parameter, instead of doubling the number of parameters. “}}
At Nearly 90, ‘Super Bowl’ Stock Analyst has a streak going – WSJ
January 18, 2016SuperBowl Stock Analyst has a streak http://www.wsj.com/articles/at-nearly-90-super-bowl-stock-analyst-has-a-streak-going-1452482753 #Statistical Frankenstein concept from Wall Street perhaps useful for genomics
A New Initiative on Precision Medicine — NEJM
September 8, 2015A New Initiative on Precision Medicine
http://www.nejm.org/doi/full/10.1056/NEJMp1500523 Notable: focus on #cancergenomics & mention of endophenotypes & #QS data
Francis S. Collins, M.D., Ph.D., and Harold Varmus, M.D.
N Engl J Med 2015; 372:793-795February 26, 2015DOI: 10.1056/NEJMp1500523
QT:{{”
“These features make efforts to improve the ways we anticipate, prevent, diagnose, and treat cancers both urgent and promising. Realizing that promise, however, will require the many different efforts reflected in the President’s initiative. To achieve a deeper understanding of cancers and discover additional tools for molecular diagnosis, we will need to analyze many more cancer genomes. ….
The cancer-focused component of this initiative will be designed to address some of the obstacles that have already been encountered in “precision oncology”: unexplained drug resistance, genomic
heterogeneity of tumors, insufficient means for monitoring responses and tumor recurrence, and limited knowledge about the use of drug combinations.
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The initiative’s second component entails pursuing research advances that will enable better assessment of disease risk, understanding of disease mechanisms, and prediction of optimal therapy for many more diseases, with the goal of expanding the benefits of precision medicine into myriad aspects of health and health care.
The initiative will encourage and support the next generation of scientists to develop creative new approaches for detecting, measuring, and analyzing a wide range of biomedical information — including molecular, genomic, cellular, clinical, behavioral, physiological, and environmental parameters. Many possibilities for future applications spring to mind: today’s blood counts might be replaced by a census of hundreds of distinct types of immune cells; data from mobile devices might provide real-time monitoring of glucose, blood pressure, and cardiac rhythm; genotyping might reveal particular genetic variants that confer protection against specific diseases…
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